Facial expression plays a significant role in human communication. It is considered the single most important cue in the psychology of emotion. Automatic recognition of emotion from images of human facial expression has been an interesting and challenging problem for the past 30 years. Aiming toward the applications of human behavior analysis, human-human interaction, and human-computer interaction, this topic has recently drawn even more attention.
A literature review shows that early-stage research on facial expression recognition focused on static images. Both feature-based and template-based approaches were investigated. Recently, researchers have been using image sequences or video data in order to develop automated expression recognition systems. As demonstrated in the fields of computer vision and psychology, various types of dynamic information, such as dynamic appearance and dynamic geometry, can be crucial for the recognition of human expressions.
However, extracting the facial dynamics from an expression sequence is not a trivial problem. For example, there can be two critical questions: first, is how to aggregate the dynamic information from expressions or varying lengths and to create features with fixed length, and second is how to perform alignment since capturing the dynamics requires near perfect alignment for the head pose and facial features. The inherent challenge for facial expression recognition is the dilemma between compensating the rigid motion or the head pose and extracting the nonrigid motion of facial muscles. For example, most existing algorithms and real-time computer programs are only capable of analyzing a frontal face with a near upright angle. This is not due to the failure to detect a face but due to the failure to register the detected face reasonably in a video.
In addition, recognition of the make and model of vehicles has generated interest in recent years. However, the majority of the work has been focused on appearance-based methods. Vehicle logos provide an alternate approach for the recognition. However, the resolution of surveillance videos is insufficient for direct recognition of logos. This disclosure proposes a super-resolution approach for vehicle logos to improve the recognition rate.
Super-resolution (SR) algorithms produce high-resolution (HR) image from low-resolution (LR) input. Accurate SR reconstruction is usually difficult and is an ill-posed image-processing problem. The existing SR algorithms can be roughly categorized into two classes based on the types of input.
The SR methods in the first class take multiple images as input. Usually, registration is performed first to align the input images. Super-resolution or interpolation is carried out subsequently to fuse multiple aligned LR images to get a HR output. These methods are based on the assumption that the LR inputs can be generated by warping and downsampling the super-resolved image. However, when the magnification increases, this assumption becomes weaker.
The SR methods in the second class use single LR image as input to infer the HR output. With the general idea that the relationship between the HR images and the LR images can be learned from examples, many methods in this class require a training step. Glasner et al. avoided using a set of training images by exploring the rich patterns in a single image. Besides, advanced interpolation algorithms without training have also been proposed which outperform the conventional interpolation techniques. For highly structural images such as vehicle logos, it is natural to develop a learning based SR approach where the model is trained from a set of similar images. Inspired by the recent success in super-resolving face images, which are also highly structural using manifold learning techniques, it would be desirable to establish a method in the subspaces that cater to the specific application for vehicle logo super-resolution. For example, one assumption is that the HR and LR manifolds have similar structure, which is locally linear and smooth. In accordance with an exemplary embodiment, canonical correlation analysis (CCA) can be applied upon the PCA coefficients of HR and LR logo images to enhance the coherence of their neighborhood structure.